Evolutionary algorithms in kinematic design of robotic systems

Chocron, Olivier; Bidaud, Philippe

doi:10.1109/iros.1997.655148

Cited by 29 publications

(21 citation statements)

References 15 publications

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“…This method yielded better results compared to two-level GA and multichromosome evolutionary algorithm; however, it still lacks optimality. Additionally, the increase in the number of design variables increases the search space exponentially that results in making this algorithm insufficient [19].…”

Section: Modular Roboticsmentioning

confidence: 99%

Evolutionary Modular Robotics: Survey and Analysis

Alattas

Patel

Sobh

2018

J Intell Robot Syst

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This paper surveys various applications of artificial evolution in the field of modular robots. Evolutionary robotics aims to design autonomous adaptive robots automatically that can evolve to accomplish a specific task while adapting to environmental changes. A number of studies have demonstrated the feasibility of evolutionary algorithms for generating robotic control and morphology. However, a huge challenge faced was how to manufacture these robots. Therefore, modular robots were employed to simplify robotic evolution and their implementation in real hardware. Consequently, more research work has emerged on using evolutionary computation to design modular robots rather than using traditional hand design approaches in order to avoid cognition bias. These techniques have the potential of developing adaptive robots that can achieve tasks not fully understood by human designers. Furthermore, evolutionary algorithms were studied to generate global modular robotic behaviors including; self-assembly, self-reconfiguration, self-repair, and self-reproduction. These characteristics allow modular robots to explore unstructured and hazardous environments. In order to accomplish the aforementioned evolutionary modular robotic promises, this paper reviews current research on evolutionary robotics and modular robots. The motivation behind this work is to identify the most promising methods that can lead to developing autonomous adaptive robotic systems that require the minimum task related knowledge on the designer side.

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Section: Modular Roboticsmentioning

confidence: 99%

Evolutionary Modular Robotics: Survey and Analysis

Alattas

Patel

Sobh

2018

J Intell Robot Syst

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“…Genetic algorithms have already been used in many kinematic problems [21,22]. The genetic algorithm performs an exhaustive exploration of the solution space in order find a solution.…”

Section: Solver Implementationmentioning

confidence: 99%

Design of Non-Anthropomorphic Robotic Hands for Anthropomorphic Tasks

Simo-Serra

Moreno-Noguer

Pérez-Gracia

2011

Volume 6: 35th Mechanisms and Robotics Conference, Parts a and B

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In this paper, we explore the idea of designing nonanthropomorphic multi-fingered robotic hands for tasks that replicate the motion of the human hand. Taking as input data a finite set of rigid-body positions for the five fingertips INTRODUCTIONThere are many applications for which a robotic system is needed to work in human environments and to perform tasks that are designed for the human hand. In most cases, the solution adopted for grasping and manipulation consists of anthropomorphic robotic hands, which imitate to a certain extent the topology and joint location of the human hand. See [1] for a review of applications and concept definition.It is difficult to match the complexity of the human hand, commonly accepted to have 26 degrees of freedom when counting the motion at the wrist and the pronation/supination of the forearm. The anthropomorphic design must include a complex mechanical system, actuation and sensing in a small space [2]. In order to reduce complexity, current designs limit the active degrees of freedom, through simplification of the mechanical structure or by designing underactuated hands. The design of simplified grippers limits the tasks of the robotic end-effector to some grasping and manipulation actions. Pairing these designs with some degree of underactuation and compliance, it is then possible to perform robust grasping of objects of unknown shape. Dollar and Howe [3] present a simplified, underactuated design for reliable grasping. Ciocarlie and Allen [4] optimize an underactuated, non-anthropomorphic gripper for performing a series of grasps from a database. A more thorough review on underactuated hands can be found in [5].In order to perform some of the more complex functions

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“…A simulated-annealing-based method which considers the kinematic constraints is presented in [4]. In [5], a two-level GA-based method for tasks in environments with static obstacles is proposed. This method mainly consists of a top-level GA to generate compositions from the given modules and a lower-level GA to optimize joint positions.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary cost-optimal composition synthesis of modular robots considering a given task

İçer

Hassan

El-Ayat

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Commercially available robots cannot always be adapted to arbitrary tasks or environments, particularly when the task would exceed the kinematic or dynamic limits of the robot. Modular robots offer a solution to this problem, since they can be reconfigured in various ways from a set of modules. The challenge of choosing the optimal composition for a given task, however, is hard since the search space of compositions is vast. Our approach addresses this problem: instead of finding the cost-optimal solution over all possible compositions individually, we propose a time-efficient composition synthesis method which uses evolutionary algorithms by taking taskrelated objectives into account. Simulations show that our algorithm finds the cost-optimal module composition with less computation time than other methods in the literature.

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Evolutionary algorithms in kinematic design of robotic systems

Cited by 29 publications

References 15 publications

Evolutionary Modular Robotics: Survey and Analysis

Evolutionary Modular Robotics: Survey and Analysis

Design of Non-Anthropomorphic Robotic Hands for Anthropomorphic Tasks

Evolutionary cost-optimal composition synthesis of modular robots considering a given task

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